Beating the competition with Cognitive Commerce

Bf9f3f29049791136b9b815f59e1f09c?s=47 Meanbee
April 05, 2017

Beating the competition with Cognitive Commerce

Artificial Intelligence, and particularly machine learning, is the talk of the tech industry and how it’s going to revolutionize every sector. So what is it? What do you need to know? And how can you use it as a tool to get an edge? This session will look at some of the recent developments and use cases, and how they can be used to differentiate and excel in eCommerce. Topics will range from intelligent search, to customer service channels of Facebook Messenger and Amazon Alexa, and to finding swimming pools in photos with IBM Watson.

Bf9f3f29049791136b9b815f59e1f09c?s=128

Meanbee

April 05, 2017
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  1. None
  2. Beating the Competition with Cognitive Commerce

  3. Tom Robertshaw Founder & CEO of Meanbee @bobbyshaw

  4. Meanbee • UK eCommerce Agency • Specialized in Magento •

    Technology First • Client revenues average $2-10 million
  5. Cognitive what?

  6. Artificial Intelligence • The idea that human thought can be

    replicated
  7. Machine Learning • Subset of AI • Ability to spot

    correlations between properties and effects • Supervised • Unsupervised • Reinforcement
  8. Supervised Learning • This is the one we’re quite good

    • Provide example inputs and responses • Learn correlations between them
  9. Supervised Learning Input Response Emails Spam Image Object Audio Text

    English French
  10. Why now? Computers are fast enough to train on large

    datasets in a reasonable time with the same or better accuracy than a person
  11. Cognitive Commerce • Facilitating commerce growth using machine learning with:

    • New interaction channels • More automation
  12. Cognitive Commerce • It’s about being smarter: • Being smarter

    in how we interact with customers • Being smarter in how we manage our store • Being smarter in how we spend our time
  13. Competitive Advantage • Increase budget • Work more hours •

    Work more efficiently
  14. Working Efficiently • Productivity • Working on the right tasks

    • Maintaining a good cost/benefit ratio
  15. Automation • Tasks that we don’t need to be doing

    • Tasks that weren’t previously feasible
  16. What can be automated? • A good heuristic from Andrew

    Ng: “” Anything that a human can do with less than one second of thought
  17. Automate or be automated • Estimates suggest between 9% to

    47% of jobs can be automated • Not just factory workers • Japanese Company replaced insurance claim agents with IBM Watson
  18. You already use machine learning

  19. Personalization • Machine Learning under our noses • Getting cheaper

    and easier • Product personalization popular, e.g. Nosto • Content personalization remains a challenge
  20. Fraud Detection • Used machine learning models for years •

    Accuracy will continue to increase • Become tailored to your business
  21. Shiny shiny hype

  22. Voice Assistants • Google Assistant • Amazon Alexa • Microsoft

    Cortana • Apple Siri • Samsung Bixby
  23. Conversational Commerce • New channels for Customer Service & Pre-Sales

    • Being available to customers at the right time
  24. Amazon Alexa • Estimated 5.2m sold last year • Customer

    spending increased 10% after purchase • Immersed in home life
  25. Amazon Alexa

  26. Natural Language Search • “Red waterproof jacket under $200” •

    Going beyond keywords to pull out and filter intent • Klevu are one of the leading examples of ML in search
  27. None
  28. Chat Bots • Facebook Messenger • Order Confirmation and Updates

    • Automate answers to basic support requests
  29. Advanced Segmentation • Segmenting based on product history is nice

    • Segmenting customers based on their personality and desires • Tailor the messaging to them
  30. Intelligent Remarketing • We segment to avoid blasting all customers

    • Most ML budget is focused on predicting ad clicks • Reduce remarketing wastage
  31. Predictive Infrastructure Scaling • Scale before load is an issue

    • Learn from tell-tale signs • Spin up further resources so that they’ll be ready in time
  32. Anticipatory shipping • Patented by Amazon • Shipping to a

    general area based on predicted orders • Put the final address on package during transit
  33. Anomaly Detection • Static measurements of traffic or actions aren’t

    useful • Learn typical site usage taking into account • Seasonality • Time of day • Marketing campaigns
  34. Catalog Management • Detect Inconsistencies • “Shouldn’t this product be

    in the Handbags category?” • “Cameras normally have a 360 zoom image, should this 
 product have one?”
  35. Catalog Management • Imagine only having to categorize products once

    • New products automatically assigned to categories • Perhaps pre-fill attributes based on description
  36. ML for your business today

  37. What • Step back and profile your teams daily tasks

    • Experiment with time tracking to review where time goes • Discuss processes • Identify repetitive tasks • Include a technical representative if possible
  38. What • Busyness is dangerous to your business • It

    makes us feel productive and satisfied • Where is the value really coming from? • What are we wasting time on?
  39. How • Depends on your technical resources • If you

    have some, look into APIs, e.g. IBM Watson
  40. IBM Watson • Natural language Analysis • Solr Search that

    can be trained • Personality Insights • Tone Analyser • Trade-off analytics • Image recognition
  41. None
  42. None
  43. Kyero • 100,000 properties • Initial tests show 80% accuracy

    identifying primary photo
  44. How • Supplier selection process • Choose tools that have

    incorporated ML • ML doesn’t make a product • But all great products will be using ML
  45. $2,000,000,000,000 market

  46. None